Journal of Applied Sciences

GUAN Chun-ying

2018, 36(4): 567-579. doi:10.3969/j.issn.0255-8297.2018.04.001

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Optical fibers are promising components for developing integrated devices due to their small cross section, long-distance propagation, easy integration of functional units and materials. The lab-on-fiber concept has received much attention in the field of fiber integrated optics. This article introduces surface core fibers (both exterior and interior surfaces) with strong evanescent waves. Optical properties of surface core fibers and the corresponding sensing applications are investigated. These fiber devices can be easily fabricated, and have important applications in biosensing and chemical detection.

Coordinated Multichannel Demand-Aware MAC Protocol

GAO Kai-qiang, ZHAO Hai-tao, LI Da-peng

2018, 36(4): 580-588. doi:10.3969/j.issn.0255-8297.2018.04.002

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The applications of vehicle self-organizing network are generally divided into the two types of security and non-security. Security applications mainly deal with issues such as emergency road conditions. Therefore, the delay and reliability of communication are strictly required for security applications. In this paper, we proposed a kind of demandaware medium access control protocol based on multichannel cooperative cooperation in vehicular ad hoc networks. First, the proposed protocol broadcasts the security query packets through roadside units, which send the emergency information of vehicle nodes in the control slots reserved in advance. The division of time slot is dynamically provisioned according to the demand of security data of each node. Thus, all nodes will be coordinated by RSU in order to reduce the collision probability and the delivery delay of data packets. Simulation results show that the media access control protocol proposed in this paper can improve the network throughput and reduce the end-to-end transmission delay, compared with the traditional IEEE 802.11p protocol.

Application Research of Improved Particle Swarm Algorithm in Underwater Speech Blind Separation

WANG Guang-yan, GENG Yan-xiang, CHEN Lei

2018, 36(4): 589-600. doi:10.3969/j.issn.0255-8297.2018.04.003

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A new independent component analysis (ICA) algorithm optimized from the improved particle swarm optimization (PSO) is proposed to overcome the drawbacks of the slow convergence speed and the aptness into local minimum of the PSO algorithm. The proposed method is aimed at extracting the target speech signal in the under-water noisy environment. It uses the absolute value of normalized fourth-order cumulant as an objective function. By changing the inertia factor ω and constriction factor k, particles have more adaptive ability to find out the optimal particle quickly. Comparing with the classical PSO algorithm, the proposed improved method performs faster convergence speed, better algorithm stability and superior separation effect.

Improved Color Image Encryption Algorithm Based on Quaternion Rotation

HU Man, LÜ Dong-hui, REN Yan-li

2018, 36(4): 601-610. doi:10.3969/j.issn.0255-8297.2018.04.004

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Image encryption plays one of the important roles in the field of information security. A new encryption scheme for color image based on quaternion rotation theory is proposed. The detailed definition and diagram of quaternion rotation are also given. The encryption scheme succeeds in getting the three color components of color image as a whole, and treating the encryption operation simultaneously. Original image is firstly decomposed into two of the same size of sub images, and then converted into pure quaternion representation of matrix. By using the property named quaternion rotation transformation, we can obtain a large amount of secret keys from iterations of the given initial secret key. Finally through an iterative loop system, the encryption is realized. The experimental results show that the encryption scheme can achieve great effect, strong security, and faster encryption and decryption speed.

High-Capacity Reversible Data Hiding for Encrypted Images

HE Zhi-hong, QIN Chuan, ZHOU Qing

2018, 36(4): 611-627. doi:10.3969/j.issn.0255-8297.2018.04.005

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In this paper, we propose a novel scheme of reversible data hiding in encrypted image. By using the same encryption key to encrypt the pixels in each non-overlapping block of original image, the contents of original image can be effectively masked by the content owner. The data-hider can realize data embedding in the encrypted image by exploiting the continuity characteristic of the pixels in each encrypted block and using run-length coding. After receiving the encrypted image containing extra data, with the encryption key, the receiver can obtain a directly-decrypted image which is similar to the original image; with the data-hiding key, the receiver can extract the embedded data correctly; with both encryption and data-hiding keys, the receiver can extract extra data and recover original image. Experimental results demonstrate that the proposed scheme can not only increase the hiding capacity in encrypted image, but also recover the original image without error.

Encrypted JPEG Image Retrieval Based on Dynamic BoW Model

WEI Qiu-han, LIANG Hai-hua, ZHANG Xin-peng

2018, 36(4): 628-634. doi:10.3969/j.issn.0255-8297.2018.04.006

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This paper presents an efficient and dynamic retrieval scheme for encrypted JPEG images, which is based on the histogram of AC coefficients. First, the robust bagof-word (BoW) model is constructed in the encrypted domain via the advanced k-means clustering. Second, the local features of images are transformed into the global statistical histograms by the dynamic BoW model. Finally, the cloud server uses the histograms of the encrypted images to judge similarity. Besides, dynamic update operations like insertion and deletion of images are also available, so the retrieval scheme is still robust and efficient. The results of experiments show that the proposed scheme can improve the retrieval efficiency and ensure the accuracy of retrieval at the same time, having practical application value.

Clustering Algorithm for Remote Sensing Application Requirements Based on Satisfaction Evaluation

WU Zhao-cong, XIANG Wei, LI Jun, YANG Zhi

2018, 36(4): 635-643. doi:10.3969/j.issn.0255-8297.2018.04.007

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When planning large-scale earth observation system, the traditional requirement analysis method, which just considers single user department and discusses satellite one by one, is no longer suitable. In order to solve this problem, this paper puts forward a kind of remote sensing application requirement clustering algorithm. First of all, through the analysis of the requirement criteria, we extract 5 systematical requirement criteria such as spatial resolution for the expression of structural requirements. Second, we establish requirement similarity definition based on satisfaction evaluation, and use it as a quantitative index to measure the similarity between requirements. Finally, we design the maximum and minimum distance clustering algorithms based on satisfaction degree to extract the center class requirements. The experimental results show that the method works well in merging similar requirements, and the clustering results will provide supports in earth observation system design and load development.

Hybrid Index Method Based on Quad Tree and R-Tree for DEM Reconstruction of Airborne Point Cloud

PENG Bao-jiang, ZHONG Ruo-fei, SUN Hai-li, GENG Yu-xin

2018, 36(4): 644-654. doi:10.3969/j.issn.0255-8297.2018.04.008

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Airborne laser point cloud features with quick and precise data acquisition, and it is a trend to build digital elevation model by using airborne radar data. However, the huge data volume of airborne laser scanning point cloud leads to low efficiency in processing the point cloud data. This paper proposes a hybrid spatial index method based on quad-tree and R-tree for digital elevation model (DEM) reconstruction, which is inspired by the ability for the fast segmentation of quad-tree and the self-balance of R-tree. First, the original point cloud data are used for constructing an index of external memory. Then the fulfilled regions of point cloud are imported into internal memory after traversing the index, and different regions of point cloud are filtered synchronously by means of morphological filtering method. Finally, the DEM can be constructed by regionally processing the acquired ground point data with the inverse distance weighted interpolation. The experimental result shows that this method can greatly improve the efficiency of data processing in DEM construction.

M-S-P (Mountain-Sea-Plain) Planning Technology and Spatial Optimum Allocation of Construction Land in Fujian Province

LI Tong, ZHANG Li, HAN Xiang-xu, ZHENG Yi

2018, 36(4): 655-666. doi:10.3969/j.issn.0255-8297.2018.04.009

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M-S-P (Mountain-Sea-Plain) is a combined construction space in this paper, of which "M" refers to mild slope of low mountains and hills, "S" refers to coastal land, "P" refers to inefficient construction land. This paper focuses on the problem of construction land resource shortage, and brings out an integral optimization allocation model-"M-S-P",which aims at optimizing construction land resource. Using economic development suitability and ecological security evaluation index, this paper allocates the land development scale of Fujian Province in 2020. The results indicate that:1) Economic development suitability evaluated by nighttime lights data manifests significant positive correlation with the inter-annual variation of GDP. 2) By the year 2020, 71.09% of newly increased construction land (approximately 141 000 hectares) will derive from "M", which are concentrated in the middle and western areas of Fujian Province; 28.51% of newly increased construction land (approximately 56 000 hectares) will derive from "P", which is concentrated in the eastern coastal areas; 0.39% of newly increased construction land (approximately 870 hectares) will derive from "S", which is mainly located in Jinjiang City and Dongshan County. "M-S-P" integral optimization allocation model of construction land can reasonably allocate the development space of construction land in mountainous areas.

An Intelligent Terminal Trademark Recognition Method Based on Computation Offloading

ZHANG Jin, FENG Fan, SUN Cheng-jun, GONG Xiao-li

2018, 36(4): 667-678. doi:10.3969/j.issn.0255-8297.2018.04.010

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Trademark is the mark that distinguishes enterprise brand or service, and plays an important role in spreading enterprise culture. Trademark recognition application on intelligent terminal is a computationally intensive application. The application performance is limited by the resource bottlenecks of intelligent terminal. Address to the problem, this paper presents a computation offloading method for trademark recognition application on intelligent terminal. In this method, the entire application is firstly divided into many task nodes by categorizing their usage. Then the energy consumption and execution time of each single task node are monitored and calculated, and an application cost graph can be built up. Finally, the objective functions based on minimum energy consumption, minimum executed time, and the balance between energy consumption and executed time can be derived. In the paper, offloading experiments based on these objective functions are carried out, and the experimental results show that the proposed method can improve the performance of trademark recognition application on intelligent terminal, reduce the energy consumption, and enhance user experience.

Data Classification Method of Fuzzy Weighted k-Nearest Neighbor Based on Affinity

LIU Cheng-cheng, JIANG Ying

2018, 36(4): 679-688. doi:10.3969/j.issn.0255-8297.2018.04.011

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In sample classification, the fuzzy k-nearest neighbor (FkNN) method and the associate improved classification algorithms ignore the uneven distribution of samples and the noise samples, thus are unable to reflect the differences of class sample features, resulting in the low classification accuracy. In order to overcome the limitations, a fuzzy weighted k-nearest neighbor data classification method based on affinity is proposed in this paper. Firstly, the membership of samples is calculated based on affinity among samples. Then, the feature weights of class samples are determined by the fuzzy entropy values, and k-neighbors are selected according to the weighted Euclidean distance. Finally, the samples will be classified according to the fuzzy membership of the samples belong to each class. The experimental results on the UCI datasets show that the proposed method is effective.

Research and Application on DBN for Well Log Interpretation

DUAN You-xiang, XU Dong-sheng, SUN Qi-feng, LI Yu

2018, 36(4): 689-697. doi:10.3969/j.issn.0255-8297.2018.04.012

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Well log interpretation refers to interpreting logging information into geological information, which was generally accomplished by establishing mathematical models or using the fundamental BP networks in the past. This study proposes to apply the deep belief network (DBN) to the interpretation of logging curve. We used four well log curves as input parameters, conducted the mudstone, and conducted the sandstone layering experiment and reservoir parameter prediction experiment with the DBN method. The results of experiment show that the DBN performs well in the interpretation of logging curve, with higher classification accuracy and shorter training time than that of BP algorithm.

Nonlinear Internal Iterative Predictive Control Using Multi-RBF Neural Network

JIANG Xue-ying, SU Cheng-li, SHI Hui-yuan, LI Ping, LIU Si-yu

2018, 36(4): 698-710. doi:10.3969/j.issn.0255-8297.2018.04.013

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For the complexity, strong nonlinearity and multi-variability in industrial processes, a nonlinear iterative predictive control algorithm based on radial base function (RBF) neural network is proposed. This algorithm employs multi-RBF neural network to approximate the nonlinear system, which can obtain an approximate model for the predictive model. Meanwhile in order to avoid increasing some computational burden, each predictive output along the future trajectory is expanded. Therefore the problem of nonlinear optimization is transformed into that of solving the easy quadratic programming,accordingly, thus the difficulty of solving the nonlinear differential equation in real-time online can be overcome, when deriving the predictive control law. Finally, the optimized control law can be obtained until the internal conditions are satisfied through internal iteration of t times. The simulation results for a pH process show that the proposed method is effective and feasible.

Prediction of Public Cycling Complex Network Scheduling Based on Wave Motion Modes

PENG Ya-li, ZENG Xin-yi, LÜ Ling, YANG Yu-xin, YIN Hong

2018, 36(4): 711-722. doi:10.3969/j.issn.0255-8297.2018.04.014

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In order to schedule optimized dispatching for public bicycle network nodes, a feasible and efficient solution is proposed. In this paper, the usage records of public bicycles in a public city bicycle data management center are selected and studied by means of statistical physics. We firstly establish a wave mode group of stock fluctuation variation by closely tracking the fluctuation of boundary value, and construct a complex network of multi-time series combined the established wave mode group. Then the fluctuation, variation rule and influencing factors of the fluctuation mode group are analyzed by using complex network method. The analytical results show that the proposed inventory fluctuation complex network model provides effective guidance for real-time dynamic scheduling, and can provide helpful solutions for similar problems.

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